论文标题
可解释的基于人工智能的故障诊断和钢板制造的洞察力收获
Explainable Artificial Intelligence Based Fault Diagnosis and Insight Harvesting for Steel Plates Manufacturing
论文作者
论文摘要
随着行业4.0的出现，数据科学和可解释的人工智能（XAI）在最近的文献中获得了相当大的吸引力。但是，就计算机编码和必要的数学设备而言，进入XAI的入口阈值确实很高。对于钢板的故障诊断，这项工作报告了一种将基于XAI的见解纳入高精度分类器开发的方法的方法。使用合成的少数族裔过采样技术（SMOTE）和MEDOID的概念，XAI工具的见解。已收集了ceteris peribus剖面，部分依赖和分解曲线。此外，还从优化的随机森林和关联规则挖掘中提取了以当时规则的形式的见解。将所有洞察力纳入单个集合分类器中，已经达到了10倍的交叉验证的94％的效果。总的来说，这项工作做出了三个主要贡献：基于基于Medioid和Smote的利用，收集见解并纳入模型开发过程的方法。其次，洞察力本身就是贡献，因为它们使钢铁制造业的人类专家受益，第三，已经开发了高精度的故障诊断分类器。
With the advent of Industry 4.0, Data Science and Explainable Artificial Intelligence (XAI) has received considerable intrest in recent literature. However, the entry threshold into XAI, in terms of computer coding and the requisite mathematical apparatus, is really high. For fault diagnosis of steel plates, this work reports on a methodology of incorporating XAI based insights into the Data Science process of development of high precision classifier. Using Synthetic Minority Oversampling Technique (SMOTE) and notion of medoids, insights from XAI tools viz. Ceteris Peribus profiles, Partial Dependence and Breakdown profiles have been harvested. Additionally, insights in the form of IF-THEN rules have also been extracted from an optimized Random Forest and Association Rule Mining. Incorporating all the insights into a single ensemble classifier, a 10 fold cross validated performance of 94% has been achieved. In sum total, this work makes three main contributions viz.: methodology based upon utilization of medoids and SMOTE, of gleaning insights and incorporating into model development process. Secondly the insights themselves are contribution, as they benefit the human experts of steel manufacturing industry, and thirdly a high precision fault diagnosis classifier has been developed.
